Explore Workflows

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/scatter-valuefrom-inputs-wf1.cwl

Branch/Commit ID: ea9f8634e41824ac3f81c3dde698d5f0eef54f1b

https://github.com/common-workflow-language/common-workflow-language.git

Path: v1.0/v1.0/scatter-wf4.cwl

Branch/Commit ID: 17695244222b0301b37cb749fe4a8d89622cd1ad

Packed ID: main

Sequence reads are first cleaned from adapters and transformed into fully bisulfite-converted forward (C->T) and reverse read (G->A conversion of the forward strand) versions, before they are aligned to similarly converted versions of the genome (also C->T and G->A converted). Sequence reads that produce a unique best alignment from the four alignment processes against the bisulfite genomes (which are running in parallel) are then compared to the normal genomic sequence and the methylation state of all cytosine positions in the read is inferred. A read is considered to align uniquely if an alignment has a unique best alignment score (as reported by the AS:i field). If a read produces several alignments with the same number of mismatches or with the same alignment score (AS:i field), a read (or a read-pair) is discarded altogether. On the next step we extract the methylation call for every single C analysed. The position of every single C will be written out to a new output file, depending on its context (CpG, CHG or CHH), whereby methylated Cs will be labelled as forward reads (+), non-methylated Cs as reverse reads (-). The output of the methylation extractor is then transformed into a bedGraph and coverage file. The bedGraph counts output is then used to generate a genome-wide cytosine report which reports the number on every single CpG (optionally every single cytosine) in the genome, irrespective of whether it was covered by any reads or not. As this type of report is informative for cytosines on both strands the output may be fairly large (~46mn CpG positions or >1.2bn total cytosine positions in the human genome).

https://github.com/datirium/workflows.git

Path: workflows/bismark-methylation-pe.cwl

Branch/Commit ID: 822a07cd6937faa4be377b0cac8780f52c817faf

https://github.com/common-workflow-language/cwltool.git

Path: tests/wf/secret_wf.cwl

Branch/Commit ID: efd59864c24d97e6d0d1d273025d3ef9003fa44d

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/docm_cle.cwl

Branch/Commit ID: ffab5424bb8b5905aecf6f8e2e6387da7f3df562

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/strelka_process_vcf.cwl

Branch/Commit ID: 86fbeb95ef85111f3b4c6bc2bba8f06cef64e157

Inspect provided directory and return filenames. Generate a new directory and return it (including content).

https://github.com/common-workflow-language/cwltool.git

Path: tests/wf/directory.cwl

Branch/Commit ID: baa668bc96ade54607465d21bc6cfa15c9bff13c

https://github.com/ncbi/pgap.git

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: d87a0786b52809b36201adb7d3d3ab2b8bbbef20

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **ChIP-Seq** basic analysis workflow for a **single-read** experiment with Trim Galore. The pipeline was adapted for ATAC-Seq single-read data analysis by updating genome coverage step. _Trim Galore_ is a wrapper around [Cutadapt](https://github.com/marcelm/cutadapt) and [FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data. In outputs it returns coordinate sorted BAM file alongside with index BAI file, quality statistics of the input FASTQ file, reads coverage in a form of BigWig file, peaks calling data in a form of narrowPeak or broadPeak files, islands with the assigned nearest genes and region type, data for average tag density plot (on the base of BAM file). Workflow starts with step *fastx\_quality\_stats* from FASTX-Toolkit to calculate quality statistics for input FASTQ file. At the same time `bowtie` is used to align reads from input FASTQ file to reference genome *bowtie\_aligner*. The output of this step is unsorted SAM file which is being sorted and indexed by `samtools sort` and `samtools index` *samtools\_sort\_index*. Based on workflow’s input parameters indexed and sorted BAM file can be processed by `samtools rmdup` *samtools\_rmdup* to get rid of duplicated reads. If removing duplicates is not required the original input BAM and BAI files return. Otherwise step *samtools\_sort\_index\_after\_rmdup* repeat `samtools sort` and `samtools index` with BAM and BAI files. Right after that `macs2 callpeak` performs peak calling *macs2\_callpeak*. On the base of returned outputs the next step *macs2\_island\_count* calculates the number of islands and estimated fragment size. If the last one is less that 80bp (hardcoded in the workflow) `macs2 callpeak` is rerun again with forced fixed fragment size value (*macs2\_callpeak\_forced*). If at the very beginning it was set in workflow input parameters to force run peak calling with fixed fragment size, this step is skipped and the original peak calling results are saved. In the next step workflow again calculates the number of islands and estimates fragment size (*macs2\_island\_count\_forced*) for the data obtained from *macs2\_callpeak\_forced* step. If the last one was skipped the results from *macs2\_island\_count\_forced* step are equal to the ones obtained from *macs2\_island\_count* step. Next step (*macs2\_stat*) is used to define which of the islands and estimated fragment size should be used in workflow output: either from *macs2\_island\_count* step or from *macs2\_island\_count\_forced* step. If input trigger of this step is set to True it means that *macs2\_callpeak\_forced* step was run and it returned different from *macs2\_callpeak* step results, so *macs2\_stat* step should return [fragments\_new, fragments\_old, islands\_new], if trigger is False the step returns [fragments\_old, fragments\_old, islands\_old], where sufix \"old\" defines results obtained from *macs2\_island\_count* step and sufix \"new\" - from *macs2\_island\_count\_forced* step. The following two steps (*bamtools\_stats* and *bam\_to\_bigwig*) are used to calculate coverage on the base of input BAM file and save it in BigWig format. For that purpose bamtools stats returns the number of mapped reads number which is then used as scaling factor by bedtools genomecov when it performs coverage calculation and saves it in BED format. The last one is then being sorted and converted to BigWig format by bedGraphToBigWig tool from UCSC utilities. To adapt the pipeline for ATAC-Seq data analysis we calculate genome coverage using only the first 9 bp from every read. Step *get\_stat* is used to return a text file with statistics in a form of [TOTAL, ALIGNED, SUPRESSED, USED] reads count. Step *island\_intersect* assigns genes and regions to the islands obtained from *macs2\_callpeak\_forced*. Step *average\_tag\_density* is used to calculate data for average tag density plot on the base of BAM file.

https://github.com/datirium/workflows.git

Path: workflows/trim-atacseq-se.cwl

Branch/Commit ID: c5bae2ca862c764911b83d1f15ff6af4e2a0db28

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/basename-fields-test.cwl

Branch/Commit ID: cd779a90a4336563dcf13795111f502372c6af83